Tessellation growth models for polycrystalline microstructures

K. Teferra and L. Graham-Brady. Computational Materials Science, Vol. 102, pages 57-67 (2015). 

This article co-authored by Kirubel Teferra and Lori Graham-Brady, proposed novel models to represent and parametrize random morphology of polycrystalline microstructures.


  • Quantitatively evaluates a tessellation model’s ability to represent polycrystalline morphology.
  • Introduces a reliable forward model for polycrystalline morphology.
  • Simulates statistically equivalent polycrystalline microstructures using marked point process.

Kirubel's Figure 2

Above is Figure 2, page 60, from Computational Materials Science, Vol 102, which depicted the reconstruction of microstructure of IN100 Nickel superalloy data set along with best-fit realizations of tessellation models: (a) IN100 reconstructed data, (b) best-fit EGT realization, (c) best-fit SGT realization and (d) best-fit VT realization.

Center for Excellence on Integrated Materials Modeling